@article {152, title = {Genetic and non-genetic factors affecting the expression of COVID-19-relevant genes in the large airway epithelium.}, journal = {Genome Med}, volume = {13}, year = {2021}, month = {2021 04 21}, pages = {66}, abstract = {

BACKGROUND: The large airway epithelial barrier provides one of the first lines of defense against respiratory viruses, including SARS-CoV-2 that causes COVID-19. Substantial inter-individual variability in individual disease courses is hypothesized to be partially mediated by the differential regulation of the genes that interact with the SARS-CoV-2 virus or are involved in the subsequent host response. Here, we comprehensively investigated non-genetic and genetic factors influencing COVID-19-relevant bronchial epithelial gene expression.

METHODS: We analyzed RNA-sequencing data from bronchial epithelial brushings obtained from uninfected individuals. We related ACE2 gene expression to host and environmental factors in the SPIROMICS cohort of smokers with and without chronic obstructive pulmonary disease (COPD) and replicated these associations in two asthma cohorts, SARP and MAST. To identify airway biology beyond ACE2 binding that may contribute to increased susceptibility, we used gene set enrichment analyses to determine if gene expression changes indicative of a suppressed airway immune response observed early in SARS-CoV-2 infection are also observed in association with host factors. To identify host genetic variants affecting COVID-19 susceptibility in SPIROMICS, we performed expression quantitative trait (eQTL) mapping and investigated the phenotypic associations of the eQTL variants.

RESULTS: We found that ACE2 expression was higher in relation to active smoking, obesity, and hypertension that are known risk factors of COVID-19 severity, while an association with interferon-related inflammation was driven by the truncated, non-binding ACE2 isoform. We discovered that expression patterns of a suppressed airway immune response to early SARS-CoV-2 infection, compared to other viruses, are similar to patterns associated with obesity, hypertension, and cardiovascular disease, which may thus contribute to a COVID-19-susceptible airway environment. eQTL mapping identified regulatory variants for genes implicated in COVID-19, some of which had pheWAS evidence for their potential role in respiratory infections.

CONCLUSIONS: These data provide evidence that clinically relevant variation in the expression of COVID-19-related genes is associated with host factors, environmental exposures, and likely host genetic variation.

}, keywords = {Adult, Aged, Aged, 80 and over, Angiotensin-Converting Enzyme 2, Asthma, Bronchi, Cardiovascular Diseases, COVID-19, Gene Expression, Genetic Variation, Humans, Middle Aged, Obesity, Pulmonary Disease, Chronic Obstructive, Quantitative Trait Loci, Respiratory Mucosa, Risk Factors, SARS-CoV-2, Smoking}, issn = {1756-994X}, doi = {10.1186/s13073-021-00866-2}, author = {Kasela, Silva and Ortega, Victor E and Martorella, Molly and Garudadri, Suresh and Nguyen, Jenna and Ampleford, Elizabeth and Pasanen, Anu and Nerella, Srilaxmi and Buschur, Kristina L and Barjaktarevic, Igor Z and Barr, R Graham and Bleecker, Eugene R and Bowler, Russell P and Comellas, Alejandro P and Cooper, Christopher B and Couper, David J and Criner, Gerard J and Curtis, Jeffrey L and Han, MeiLan K and Hansel, Nadia N and Hoffman, Eric A and Kaner, Robert J and Krishnan, Jerry A and Martinez, Fernando J and McDonald, Merry-Lynn N and Meyers, Deborah A and Paine, Robert and Peters, Stephen P and Castro, Mario and Denlinger, Loren C and Erzurum, Serpil C and Fahy, John V and Israel, Elliot and Jarjour, Nizar N and Levy, Bruce D and Li, Xingnan and Moore, Wendy C and Wenzel, Sally E and Zein, Joe and Langelier, Charles and Woodruff, Prescott G and Lappalainen, Tuuli and Christenson, Stephanie A} } @article {83, title = {Genetic regulatory variation in populations informs transcriptome analysis in rare disease.}, journal = {Science}, volume = {366}, year = {2019}, month = {2019 10 18}, pages = {351-356}, abstract = {

Transcriptome data can facilitate the interpretation of the effects of rare genetic variants. Here, we introduce ANEVA (analysis of expression variation) to quantify genetic variation in gene dosage from allelic expression (AE) data in a population. Application of ANEVA to the Genotype-Tissues Expression (GTEx) data showed that this variance estimate is robust and correlated with selective constraint in a gene. Using these variance estimates in a dosage outlier test (ANEVA-DOT) applied to AE data from 70 Mendelian muscular disease patients showed accuracy in detecting genes with pathogenic variants in previously resolved cases and led to one confirmed and several potential new diagnoses. Using our reference estimates from GTEx data, ANEVA-DOT can be incorporated in rare disease diagnostic pipelines to use RNA-sequencing data more effectively.

}, issn = {1095-9203}, doi = {10.1126/science.aay0256}, author = {Mohammadi, Pejman and Castel, Stephane E and Cummings, Beryl B and Einson, Jonah and Sousa, Christina and Hoffman, Paul and Donkervoort, Sandra and Jiang, Zhuoxun and Mohassel, Payam and Foley, A Reghan and Wheeler, Heather E and Im, Hae Kyung and Bonnemann, Carsten G and MacArthur, Daniel G and Lappalainen, Tuuli} } @article {64, title = {Genomic Analysis in the Age of Human Genome Sequencing.}, journal = {Cell}, volume = {177}, year = {2019}, month = {2019 Mar 21}, pages = {70-84}, abstract = {

Affordable genome sequencing technologies promise to revolutionize the field of human genetics by enabling comprehensive studies that interrogate all classes of genome variation, genome-wide, across the entire allele frequency spectrum. Ongoing projects worldwide are sequencing many thousands-and soon millions-of human genomes as part of various gene mapping studies, biobanking efforts, and clinical programs. However, while genome sequencing data production has become routine, genome analysis and interpretation remain challenging endeavors with many limitations and caveats. Here, we review the current state of technologies for genetic variant discovery, genotyping, and functional interpretation and discuss the prospects for future advances. We focus on germline variants discovered by whole-genome sequencing, genome-wide functional genomic approaches for predicting and measuring variant functional effects, and implications for studies of common and rare human disease.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2019.02.032}, author = {Lappalainen, Tuuli and Scott, Alexandra J and Brandt, Margot and Hall, Ira M} } @article {25, title = {Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations.}, journal = {Nat Commun}, volume = {8}, year = {2017}, month = {2017 Aug 16}, pages = {266}, abstract = {

The immune system plays a major role in human health and disease, and understanding genetic causes of interindividual variability of immune responses is vital. Here, we isolate monocytes from 134 genotyped individuals, stimulate these cells with three defined microbe-associated molecular patterns (LPS, MDP, and 5{\textquoteright}-ppp-dsRNA), and profile the transcriptomes at three time points. Mapping expression quantitative trait loci (eQTL), we identify 417 response eQTLs (reQTLs) with varying effects between conditions. We characterize the dynamics of genetic regulation on early and late immune response and observe an enrichment of reQTLs in distal cis-regulatory elements. In addition, reQTLs are enriched for recent positive selection with an evolutionary trend towards enhanced immune response. Finally, we uncover reQTL effects in multiple GWAS loci and show a stronger enrichment for response than constant eQTLs in GWAS signals of several autoimmune diseases. This demonstrates the importance of infectious stimuli in modifying genetic predisposition to disease.Insight into the genetic influence on the immune response is important for the understanding of interindividual variability in human pathologies. Here, the authors generate transcriptome data from human blood monocytes stimulated with various immune stimuli and provide a time-resolved response eQTL map.

}, keywords = {Acetylmuramyl-Alanyl-Isoglutamine, Adjuvants, Immunologic, Adolescent, Adult, Autoimmune Diseases, Gene Expression, Gene Expression Profiling, Gene Expression Regulation, Genetic Predisposition to Disease, Healthy Volunteers, Humans, Indicators and Reagents, Lipids, Lipopolysaccharides, Male, Monocytes, Quantitative Trait Loci, Regulatory Sequences, Nucleic Acid, RNA, Double-Stranded, RNA, Messenger, Young Adult}, issn = {2041-1723}, doi = {10.1038/s41467-017-00366-1}, author = {Kim-Hellmuth, Sarah and Bechheim, Matthias and P{\"u}tz, Benno and Mohammadi, Pejman and N{\'e}d{\'e}lec, Yohann and Giangreco, Nicholas and Becker, Jessica and Kaiser, Vera and Fricker, Nadine and Beier, Esther and Boor, Peter and Castel, Stephane E and N{\"o}then, Markus M and Barreiro, Luis B and Pickrell, Joseph K and M{\"u}ller-Myhsok, Bertram and Lappalainen, Tuuli and Schumacher, Johannes and Hornung, Veit} }